### Clear the workspace
rm(list=ls())
### load the libraries
library(BioGeoBEARS)
library(optimx)
library(FD) # for FD::maxent() (make sure this is up-to-date)
Loading required package: ade4
Loading required package: geometry
Loading required package: magic
Loading required package: abind
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-3
Attaching package: ‘vegan’
The following object is masked from ‘package:ade4’:
cca
library(snow)
library(parallel)
Attaching package: ‘parallel’
The following objects are masked from ‘package:snow’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, clusterSplit, makeCluster, parApply, parCapply, parLapply, parRapply, parSapply,
splitIndices, stopCluster
### load the updates recommended by the developer
source("http://phylo.wdfiles.com/local--files/biogeobears/cladoRcpp.R") # (needed now that traits model added; source FIRST!)
Loading required package: roxygen2
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_add_fossils_randomly_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_basics_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_calc_transition_matrices_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_classes_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_detection_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_DNA_cladogenesis_sim_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_extract_Qmat_COOmat_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_generics_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_models_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_on_multiple_trees_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_plots_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_readwrite_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_simulate_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_SSEsim_makePlots_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_SSEsim_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_stochastic_mapping_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_stratified_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_univ_model_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/calc_uppass_probs_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/calc_loglike_sp_v01.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/get_stratified_subbranch_top_downpass_likelihoods_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/runBSM_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/stochastic_map_given_inputs.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/summarize_BSM_tables_v1.R")
source("http://phylo.wdfiles.com/local--files/biogeobears/BioGeoBEARS_traits_v1.R") # added traits model
calc_loglike_sp = compiler::cmpfun(calc_loglike_sp_prebyte) # crucial to fix bug in uppass calculations
calc_independent_likelihoods_on_each_branch = compiler::cmpfun(calc_independent_likelihoods_on_each_branch_prebyte)
Taking all of the above into account, we will be using Maximum Clade Credibility (MCC) trees made using our MrBayes output instead of the usual MrBayes consensus trees. Treeannotator is a tool commonly used to make MCC trees from BEAST analyses but it is not compatible with much of the metadata written into MrBayes formatted nexus tree files. In order to generate the MCC trees, it is necessary to perform some preliminary reformatting of tree files, ready for use in Treeannotator. The following cell performs most of this task.
root_path<-"MrBayes/Tree_files/MrBayes_treesets/"
my_files<-list.files(path = root_path, pattern = "*GTRIG.nex.run.{2,3}t")
for(i in 1:length(my_files)){
trees_in<-read.nexus(file = paste(root_path, my_files[i], sep =""))
write.nexus(trees_in[2501:10001], file = paste(root_path,"run",i,"_out.t", sep=''))
}
In addition to Treeannotator being incompatible with MrBayes format nexus files, BioGeoBEARS is additionally incompatible with the nexus formatted trees from Treeannotator. BioGeoBEARS also does not use standard phylo class objects in R so we have to convert all of our nexus files to newick format by importing the nexus trees and then exporting them in newick format. The next cell performs this task.
### read in the MCC Cratopus tree from tree annotator
for( i in 1:length(my_files)){
my.nexus.tree<-read.nexus(paste("MrBayes/Tree_files/MrBayes_treesets/run",i,"_out.t.MCC", sep=""))
### read in the list of names
name<-read.csv("Metadata/Multilocus_names.csv")
### read.csv turns text into factors, this gets messy later when plotting
### so make it character data
name<-data.frame(lapply(name, as.character), stringsAsFactors=FALSE)
#my.nexus.tree$tip.label<-name$Alt_label[match(my.nexus.tree$tip.label,name$Name)]
### BioGeoBEARS doesn't like nexus files so we export this as .newick format and reimport it.
write.tree(my.nexus.tree, file=paste("MrBayes/Tree_files/MrBayes_treesets/run",i,"_MCC.newick", sep=""))
}
my_newicks<-list.files(path = root_path, pattern = "*.newick")
teststable = NULL
for(i in my_newicks){
# "trfn" = "tree file name"
trfn <- paste(root_path,i, sep ="")
# Look at the raw Newick file:
moref(trfn)
# Look at your phylogeny:
my.tree <- read.tree(trfn)
### provide a path to the geography file
geogfn <- "Metadata/BioGeoBEARS_islands.txt"
### provide a path to the distance matrix
dist.mat<-"Metadata/BioGeoBEARS_dist.txt"
# Look at the raw geography text file:
moref(geogfn)
# Look at your geographic range data:
tipranges = getranges_from_LagrangePHYLIP(lgdata_fn=geogfn)
tipranges
# Set the maximum number of areas any species may occupy; this cannot be larger
# than the number of areas you set up, but it can be smaller.
max_range_size = 2
#######################################################
# DEC AND DEC+J ANALYSES
#NOTE: The BioGeoBEARS "DEC" model is identical with the Lagrange DEC model, and should return identical ML estimates of parameters, and the same log-likelihoods, for the same datasets. Ancestral state probabilities at nodes will be slightly different, since BioGeoBEARS is reporting the ancestral state probabilities under the global ML model, and Lagrange is reporting ancestral state probabilities after re-optimizing the likelihood after fixing the state at each node. These will be similar, but not identical. See Matzke (2014), Systematic Biology, for discussion. Also see Matzke (2014) for presentation of the DEC+J model.
### Run DEC
#Intitialize a default model (DEC model)
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
# Give BioGeoBEARS the location of the phylogeny Newick file
BioGeoBEARS_run_object$trfn = trfn
# Give BioGeoBEARS the location of the geography text file
BioGeoBEARS_run_object$geogfn = geogfn
# Input the maximum range size
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc. (see Massana et al.)
# also search script on "include_null_range" for other places to change
#Speed options and multicore processing if desired
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = TRUE # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 1
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DEC model
# (nothing to do; defaults)
# Look at the BioGeoBEARS_run_object; it's just a list of settings etc.
BioGeoBEARS_run_object
# This contains the model object
BioGeoBEARS_run_object$BioGeoBEARS_model_object
# This table contains the parameters of the model
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table
# Run this to check inputs. Read the error messages if you get them!
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
### These analyses spit out some R.data files so make a subdirectory od /Data to save them in
dir.create("Metadata/BioGeoBEARS_save_files", showWarnings = FALSE)
# For a slow analysis, run once, then set runslow=FALSE to just
# load the saved result.
runslow = TRUE
resfn = "Metadata/BioGeoBEARS_save_files/Cratopus_DEC.Rdata"
if (runslow)
{
res = bears_optim_run(BioGeoBEARS_run_object)
res
save(res, file=resfn)
resDEC = res
} else {
# Loads to "res"
load(resfn)
resDEC = res
}
### Run DEC+J
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc. (see Massana et al.)
# also search script on "include_null_range" for other places to change
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = TRUE # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 1
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DEC+J model
# Get the ML parameter values from the 2-parameter nested model
# (this will ensure that the 3-parameter model always does at least as good)
dstart = resDEC$outputs@params_table["d","est"]
estart = resDEC$outputs@params_table["e","est"]
jstart = 0.0001
# Input starting values for d, e
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","init"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","est"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","init"] = estart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","est"] = estart
# Add j as a free parameter
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = jstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = jstart
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
resfn = "Metadata/BioGeoBEARS_save_files/Cratopus_DEC+J.Rdata"
runslow = TRUE
if (runslow)
{
res = bears_optim_run(BioGeoBEARS_run_object)
res
save(res, file=resfn)
resDECj = res
} else {
# Loads to "res"
load(resfn)
resDECj = res
}
# CALCULATE SUMMARY STATISTICS TO COMPARE
# DEC, DEC+J, DIVALIKE, DIVALIKE+J, BAYAREALIKE, BAYAREALIKE+J
# REQUIRED READING:
#
# Practical advice / notes / basic principles on statistical model
# comparison in general, and in BioGeoBEARS:
# http://phylo.wikidot.com/advice-on-statistical-model-comparison-in-biogeobears
#########################################################################
# Set up empty tables to hold the statistical results
restable = NULL
#######################################################
# Statistics -- DEC vs. DEC+J
#######################################################
# We have to extract the log-likelihood differently, depending on the
# version of optim/optimx
LnL_2 = get_LnL_from_BioGeoBEARS_results_object(resDEC)
LnL_1 = get_LnL_from_BioGeoBEARS_results_object(resDECj)
numparams1 = 3
numparams2 = 2
stats = AICstats_2models(LnL_1, LnL_2, numparams1, numparams2)
stats
# DEC, null model for Likelihood Ratio Test (LRT)
res2 = extract_params_from_BioGeoBEARS_results_object(results_object=resDEC, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# DEC+J, alternative model for Likelihood Ratio Test (LRT)
res1 = extract_params_from_BioGeoBEARS_results_object(results_object=resDECj, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# The null hypothesis for a Likelihood Ratio Test (LRT) is that two models
# confer the same likelihood on the data. See: Brian O'Meara's webpage:
# http://www.brianomeara.info/tutorials/aic
# ...for an intro to LRT, AIC, and AICc
rbind(res2, res1)
tmp_tests = conditional_format_table(stats)
restable = rbind(restable, res2, res1)
teststable = rbind(teststable, tmp_tests)
#write.csv(x = teststable, file = "Metadata/BioGeoBEARS_save_files/teststable.csv")
#######################################################
# PDF plots
#######################################################
pdffn = paste("Metadata/BioGeoBEARS_save_files/Cratopus_DEC+J_",i,".pdf",sep="")
pdf(pdffn, width=6, height=12)
#######################################################
# Plot ancestral states - DEC
#######################################################
#analysis_titletxt ="BioGeoBEARS DEC on Cratopus MCC tree"
# Setup
#results_object = resDEC
#scriptdir = np(system.file("extdata/a_scripts", package="BioGeoBEARS"))
# States
#res2 = plot_BioGeoBEARS_results(results_object, analysis_titletxt, addl_params=list("j"), plotwhat="text", label.offset=0.45, tipcex=0.7, statecex=0.7, splitcex=0.6, titlecex=0.8, plotsplits=TRUE, cornercoords_loc=scriptdir, include_null_range=TRUE, tr=my.tree, tipranges=tipranges)
# Pie chart
#plot_BioGeoBEARS_results(results_object, analysis_titletxt, addl_params=list("j"), plotwhat="pie", label.offset=0.45, tipcex=0.7, statecex=0.7, splitcex=0.6, titlecex=0.8, plotsplits=TRUE, cornercoords_loc=scriptdir, include_null_range=TRUE, tr=my.tree, tipranges=tipranges)
#######################################################
# Plot ancestral states - DECJ
#######################################################
analysis_titletxt ="BioGeoBEARS DEC+J on Cratopus MCC tree"
# Setup
results_object = resDECj
scriptdir = np(system.file("extdata/a_scripts", package="BioGeoBEARS"))
# States
#res1 = plot_BioGeoBEARS_results(results_object, analysis_titletxt, addl_params=list("j"), plotwhat="text", label.offset=0.45, tipcex=0.7, statecex=0.7, splitcex=0.6, titlecex=0.8, plotsplits=TRUE, cornercoords_loc=scriptdir, include_null_range=TRUE, tr=my.tree, tipranges=tipranges)
# Pie chart
plot_BioGeoBEARS_results(results_object, analysis_titletxt, addl_params=list("j"), plotwhat="pie", label.offset=0.45, tipcex=0.7, statecex=0.7, splitcex=0.6, titlecex=0.8, plotsplits=TRUE, cornercoords_loc=scriptdir, include_null_range=TRUE, tr=my.tree, tipranges=tipranges)
dev.off() # Turn off PDF
cmdstr = paste("open ", pdffn, sep="")
system(cmdstr) # Plot it
}
Read 1 item
((((((((((((A01_Ald3:0.008673824523,((((A07_Ari1:0.001939635629,G02_Csne2:0.00193963563):0.0006928410352,H09_Csn1:0.002632476666):0.0009824733934,C05_Dig2:0.003614950058):0.001761492939,B02_Sil4:0.005376442994):0.003297381513):0.001290202801,G01_Ald9:0.009964027327):0.0014759722,((C09_EUR51:0.005157500983,F06_JDN39:0.005157500987):0.004167330855,G10_GGL2:0.009324831871):0.002115167667):0.007540133133,H05_GRA5:0.01898013262):0.0009110454921,(E04_Mau270:0.009384266905,Reu_punc:0.009384266904):0.01050691125):0.004925987797,F01_Moh1:0.02481716591):0.06445816508,(((C04_Reu3837:0.02580294072,C06_Reu2000:0.02580294072):0.01146798028,D04_Reu4006:0.03727092117):0.02608285047,E09_Reu268:0.06335377162):0.0259215594):0.02215132349,(B03_Mau3189:0.06560029725,(((C02_Mau3202:0.02481563704,H08_Mau3333:0.02481563703):0.0121041778,H07_Mau3099:0.03691981487):0.01983920166,(D02_Mau3001:0.03236644963,Reu_mur:0.03236644963):0.02439256702):0.008841280641):0.04582635731):0.02568972296,(((A02_Reu3926:0.07958243059,G07_Mau3572:0.07958243059):0.02768613656,(F09_Mau3649:0.01628655193,H01_Reu3824:0.01628655193):0.09098201539):0.02025828659,((((A06_Mau3268:0.02910920102,B04_Reu3461:0.02910920102):0.07661254171,C08_Mau3856:0.1057217429):0.00906424269,(B08_Mau256:0.07827152223,((D09_Reu2041:0.009113509653,H06_Mau3721:0.009113509654):0.05374797325,(E06_Mau3680:0.04935218412,F07_Mau3606:0.04935218411):0.01350929882):0.01541003929):0.03651446321):0.002610013764,(B06_Mau3284:0.1032267766,(D07_Rod286:0.04497461035,F10_Rod164:0.04497461035):0.05825216645):0.0141692224):0.01013085451):0.009589523703):0.008718927098,(A09_Mau3863:0.1262288299,((C10_Reu2580:0.03859140113,G08_Mau3628:0.03859140113):0.07387048619,E10_Reu3072:0.1124618875):0.01376694259):0.0196064746):0.0268859657,((((((A11_Rod56:0.04425658275,H10_Rod124:0.04425658275):0.03739857621,H03_Rod257:0.08165515909):0.02752666199,((B05_Anj4:0.02216827378,G05_Anj3:0.02216827378):0.03704542698,G03_GRA10:0.0592137008):0.04996812024):0.02633790532,(((B01_Dig5:0.006271301494,D06_Pra6:0.006271301492):0.004311156058,B11_Mah1:0.01058245752):0.05614653576,F08_Dig6:0.06672899326):0.06879073317):0.01983448821,((B10_Reu2320:0.01327522391,E07_Mau3707:0.01327522391):0.05624959585,D01_Reu5116:0.06952481968):0.08582939498):0.01010052273,G09_Mau3357:0.1654547378):0.007266532933):0.008694839477,A04_Mad502:0.1814161099):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
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Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-216.7514
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
unused control arguments ignored
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.569469136 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 0.001522257 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.000000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.000000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.000000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.000000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.000000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.000000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.000000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.000000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.000000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.000000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.000000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.000000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.000100000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.000100000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.000100000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.000100000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.000100000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.500000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.100000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.000000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.000000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-152.3922
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 3.75547
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.unused control arguments ignored
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.569469136 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.001522257 1e-12 5.00000 5.365722e-01 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.000000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.000000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.000000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.000000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.000000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.000000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.000100000 1e-05 2.99999 3.303034e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.999990000 1e-05 3.00000 2.966970e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.999990000 1e-05 2.00000 1.977980e+00 works cladogenesis: y+s
y ysv*1/3 1.000000000 1e-05 1.00000 9.889899e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.000000000 1e-05 1.00000 9.889899e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.000000000 1e-05 1.00000 9.889899e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.000100000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.000100000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.000100000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.000100000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.000100000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.500000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.100000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.000000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.000000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
Read 1 item
(((((((((((A01_Ald3:0.008792710372,((((A07_Ari1:0.001933379197,G02_Csne2:0.0019333792):0.0007324543348,H09_Csn1:0.00266583353):0.001038888909,C05_Dig2:0.003704722434):0.001731933401,B02_Sil4:0.00543665584):0.003356054522):0.001273816663,G01_Ald9:0.01006652703):0.001529188799,((C09_EUR51:0.005209677624,F06_JDN39:0.005209677625):0.004232500074,G10_GGL2:0.009442177718):0.002153538119):0.008515680088,((E04_Mau270:0.009577712127,Reu_punc:0.009577712125):0.009434895361,H05_GRA5:0.01901260749):0.001098788402):0.004862854597,F01_Moh1:0.02497425041):0.06428547782,(((C04_Reu3837:0.02576356468,C06_Reu2000:0.02576356468):0.01151496007,D04_Reu4006:0.03727852472):0.02606884977,E09_Reu268:0.06334737461):0.02591235367):0.02220182236,(B03_Mau3189:0.06577286012,(((C02_Mau3202:0.0247617774,H08_Mau3333:0.0247617774):0.01214925654,H07_Mau3099:0.03691103386):0.01992976707,(D02_Mau3001:0.03252138896,Reu_mur:0.03252138896):0.02431941206):0.008932059095):0.0456886905):0.0255492103,(((A02_Reu3926:0.07941354974,G07_Mau3572:0.07941354974):0.02777902565,(F09_Mau3649:0.01628591503,H01_Reu3824:0.01628591503):0.09090666058):0.02016819301,((((A06_Mau3268:0.02904361861,B04_Reu3461:0.02904361861):0.07684770217,C08_Mau3856:0.1058913208):0.008691532336,(B08_Mau256:0.07831514612,((D09_Reu2041:0.009054266732,H06_Mau3721:0.009054266732):0.05385356462,(E06_Mau3680:0.04940991692,F07_Mau3606:0.04940991695):0.01349791442):0.01540731481):0.03626770708):0.002703101138,(B06_Mau3284:0.1034123735,(D07_Rod286:0.04479963275,F10_Rod164:0.04479963275):0.05861274092):0.01387358077):0.01007481391):0.00964999243):0.008656374614,(A09_Mau3863:0.1264760479,((C10_Reu2580:0.03868300738,G08_Mau3628:0.03868300738):0.07418828548,E10_Reu3072:0.1128712927):0.01360475461):0.01919108812):0.02658906827,((((((A11_Rod56:0.0439500838,H10_Rod124:0.0439500838):0.03770787091,H03_Rod257:0.0816579546):0.02747009297,((B05_Anj4:0.02225832341,G05_Anj3:0.02225832341):0.03689725422,G03_GRA10:0.05915557756):0.04997247011):0.02603140248,(((B01_Dig5:0.006269648043,D06_Pra6:0.006269648049):0.004369730062,B11_Mah1:0.0106393781):0.05607881214,F08_Dig6:0.06671819025):0.06844125982):0.01967118133,((B10_Reu2320:0.01331731059,E07_Mau3707:0.01331731059):0.05610447046,D01_Reu5116:0.06942178095):0.08540885065):0.01008395569,G09_Mau3357:0.1649145874):0.007341616458):0.008576740115,A04_Mad502:0.1808329438):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-217.2799
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.8327739 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 4.9986925 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-153.9452
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.698856
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.8327739 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 4.9986925 1e-12 5.00000 1.722221e+00 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 3.359260e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.966407e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.977605e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.888025e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.888025e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.888025e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
Read 1 item
((((((((((((A01_Ald3:0.008620204365,((((A07_Ari1:0.001897595529,G02_Csne2:0.00189759553):0.0007337235041,H09_Csn1:0.002631319038):0.0009869683464,C05_Dig2:0.00361828738):0.00173778822,B02_Sil4:0.005356075604):0.003264128781):0.00129364962,G01_Ald9:0.00991385397):0.001521490975,((C09_EUR51:0.005145261897,F06_JDN39:0.005145261901):0.004222596269,G10_GGL2:0.009367858154):0.002067486788):0.007557990237,H05_GRA5:0.01899333519):0.0009452637891,(E04_Mau270:0.009437968433,Reu_punc:0.009437968433):0.01050063057):0.004820798484,F01_Moh1:0.02475939748):0.06465776195,(((C04_Reu3837:0.02594153544,C06_Reu2000:0.02594153544):0.01147648309,D04_Reu4006:0.03741801852):0.02636334812,E09_Reu268:0.06378136665):0.02563579275):0.02212111896,(B03_Mau3189:0.06544717463,(((C02_Mau3202:0.02473101608,H08_Mau3333:0.02473101608):0.01206892012,H07_Mau3099:0.03679993626):0.01987510044,(D02_Mau3001:0.03241800067,Reu_mur:0.03241800066):0.02425703604):0.008772137839):0.04609110378):0.02580853156,(((A02_Reu3926:0.07959985273,G07_Mau3572:0.07959985273):0.0277796786,(F09_Mau3649:0.01637565274,H01_Reu3824:0.01637565274):0.0910038787):0.02029271233,((((A06_Mau3268:0.02919322308,B04_Reu3461:0.02919322308):0.07701141721,C08_Mau3856:0.1062046405):0.008850497169,(B08_Mau256:0.07818430908,((D09_Reu2041:0.009050788002,H06_Mau3721:0.009050788002):0.05383685771,(E06_Mau3680:0.04933553664,F07_Mau3606:0.04933553663):0.01355210901):0.01529666339):0.03687082839):0.00258679689,(B06_Mau3284:0.1036592421,(D07_Rod286:0.04484378713,F10_Rod164:0.04484378713):0.05881545505):0.01398269218):0.01003030939):0.009674566249):0.00874063523,(A09_Mau3863:0.1265488019,((C10_Reu2580:0.03883775295,G08_Mau3628:0.03883775295):0.07439798227,E10_Reu3072:0.1132357355):0.01331306685):0.01953864299):0.02682669326,((((((A11_Rod56:0.04439128664,H10_Rod124:0.04439128664):0.03770803919,H03_Rod257:0.08209932559):0.02745526706,((B05_Anj4:0.02205474942,G05_Anj3:0.02205474942):0.03738162338,G03_GRA10:0.05943637289):0.05011822001):0.02625745353,(((B01_Dig5:0.006282334018,D06_Pra6:0.006282334013):0.004367294293,B11_Mah1:0.01064962828):0.05597352275,F08_Dig6:0.06662315107):0.0691888953):0.0195738389,((B10_Reu2320:0.01327117157,E07_Mau3707:0.01327117157):0.05615000557,D01_Reu5116:0.06942117715):0.08596470815):0.0100573546,G09_Mau3357:0.16544324):0.007470898485):0.008666152788,A04_Mad502:0.1815802906):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-216.7684
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.6974381 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 2.0633546 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-150.3111
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.314574
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.6974381 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 2.0633546 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 3.305621e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.966944e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.977963e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.889813e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.889813e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.889813e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
Read 1 item
(((((((((((A01_Ald3:0.008700284599,((((A07_Ari1:0.001953308951,G02_Csne2:0.001953308955):0.0007272353125,H09_Csn1:0.002680544263):0.0009845947979,C05_Dig2:0.003665139064):0.001734461648,B02_Sil4:0.005399600706):0.003300683898):0.001331860298,G01_Ald9:0.01003214491):0.001515695538,((C09_EUR51:0.005161141028,F06_JDN39:0.005161141024):0.004219068828,G10_GGL2:0.009380209833):0.002167630592):0.008483660929,((E04_Mau270:0.009539002996,Reu_punc:0.009539002998):0.009328815111,H05_GRA5:0.01886781814):0.001163683286):0.005075154761,F01_Moh1:0.02510665619):0.06421459464,(((C04_Reu3837:0.02582778684,C06_Reu2000:0.02582778684):0.01143375584,D04_Reu4006:0.03726154258):0.02636878585,E09_Reu268:0.06363032842):0.02569092243):0.02230932379,(B03_Mau3189:0.0657665488,(((C02_Mau3202:0.02483564405,H08_Mau3333:0.02483564404):0.01221334566,H07_Mau3099:0.0370489897):0.01982641084,(D02_Mau3001:0.03260004275,Reu_mur:0.03260004274):0.02427535777):0.00889114828):0.04586402577):0.02570871832,(((A02_Reu3926:0.07947319239,G07_Mau3572:0.07947319239):0.02791252622,(F09_Mau3649:0.01615788217,H01_Reu3824:0.01615788217):0.09122783655):0.0202382428,((((A06_Mau3268:0.02905644572,B04_Reu3461:0.02905644572):0.07662648488,C08_Mau3856:0.1056829306):0.009042928126,(B08_Mau256:0.07835054669,((D09_Reu2041:0.00915046121,H06_Mau3721:0.00915046121):0.05373961031,(E06_Mau3680:0.04924557938,F07_Mau3606:0.04924557937):0.01364449211):0.0154604752):0.03637531203):0.002731086651,(B06_Mau3284:0.1036111402,(D07_Rod286:0.0450250801,F10_Rod164:0.0450250801):0.05858606006):0.01384580527):0.01016701616):0.009715331422):0.00878308134,(A09_Mau3863:0.126591129,((C10_Reu2580:0.03871805266,G08_Mau3628:0.03871805266):0.07479989507,E10_Reu3072:0.1135179477):0.01307318158):0.01953124505):0.02692923403,((((((A11_Rod56:0.04443493278,H10_Rod124:0.04443493278):0.03762623582,H03_Rod257:0.08206116856):0.02744795873,((B05_Anj4:0.0221838449,G05_Anj3:0.0221838449):0.03740263723,G03_GRA10:0.05958648222):0.0499226452):0.0261806666,(((B01_Dig5:0.006303682375,D06_Pra6:0.006303682373):0.004318607965,B11_Mah1:0.01062229033):0.05617942412,F08_Dig6:0.06680171457):0.06888807945):0.01986881797,((B10_Reu2320:0.01332911787,E07_Mau3707:0.01332911787):0.05629927251,D01_Reu5116:0.06962839038):0.08593022171):0.01013979339,G09_Mau3357:0.1656984049):0.007353202989):0.008530391855,A04_Mad502:0.1815819998):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-217.1541
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.7132598 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 1.9934791 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-150.6819
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.299612
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.7132598 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 1.9934791 1e-12 5.00000 1.293619e+00 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 3.384661e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.966153e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.977436e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.887178e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.887178e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.887178e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Couldn't get a file descriptor referring to the console
Read 1 item
((((((((((((A01_Ald3:0.008813147632,((((A07_Ari1:0.001976229597,G02_Csne2:0.0019762296):0.0007303710702,H09_Csn1:0.002706600669):0.001012902649,C05_Dig2:0.003719503318):0.001777969149,B02_Sil4:0.005497472463):0.003315675167):0.001310114986,G01_Ald9:0.01012326258):0.001507997081,((C09_EUR51:0.005215296094,F06_JDN39:0.005215296097):0.00428870441,G10_GGL2:0.009504000554):0.002127259186):0.00751648632,H05_GRA5:0.01914774604):0.0009804727654,(E04_Mau270:0.009553682883,Reu_punc:0.009553682884):0.01057453595):0.004942927632,F01_Moh1:0.02507114638):0.06459088754,(((C04_Reu3837:0.02571797197,C06_Reu2000:0.02571797198):0.01146567689,D04_Reu4006:0.03718364899):0.02632636317,E09_Reu268:0.06351001217):0.02615202174):0.02223920036,(B03_Mau3189:0.06574145986,(((C02_Mau3202:0.0248797195,H08_Mau3333:0.02487971951):0.01209637428,H07_Mau3099:0.03697609378):0.01985611952,(D02_Mau3001:0.03259468808,Reu_mur:0.03259468808):0.02423752514):0.008909246591):0.04615977441):0.02579316725,(((A02_Reu3926:0.07998952872,G07_Mau3572:0.07998952872):0.02767396819,(F09_Mau3649:0.01649071962,H01_Reu3824:0.01649071962):0.09117277722):0.02037558902,((((A06_Mau3268:0.02942220301,B04_Reu3461:0.02942220301):0.07683819626,C08_Mau3856:0.1062603994):0.008938861822,(B08_Mau256:0.07839980841,((D09_Reu2041:0.009137980308,H06_Mau3721:0.009137980308):0.05379916105,(E06_Mau3680:0.04925794297,F07_Mau3606:0.04925794304):0.01367919829):0.0154626671):0.03679945255):0.002804346713,(B06_Mau3284:0.1038593352,(D07_Rod286:0.04496206214,F10_Rod164:0.04496206214):0.05889727327):0.01414427241):0.01003547822):0.009655315594):0.008987008303,(A09_Mau3863:0.1274928408,((C10_Reu2580:0.0388436246,G08_Mau3628:0.0388436246):0.07494298374,E10_Reu3072:0.1137866077):0.01370623298):0.01918856859):0.02664599906,((((((A11_Rod56:0.04435418992,H10_Rod124:0.04435418992):0.03769387422,H03_Rod257:0.08204806415):0.02747267396,((B05_Anj4:0.02210862238,G05_Anj3:0.02210862238):0.03730688797,G03_GRA10:0.05941551028):0.05010522763):0.02635763979,(((B01_Dig5:0.006364308082,D06_Pra6:0.00636430808):0.004327595071,B11_Mah1:0.01069190314):0.05619487768,F08_Dig6:0.06688678086):0.06899159696):0.01992886096,((B10_Reu2320:0.01330696656,E07_Mau3707:0.01330696656):0.05620824015,D01_Reu5116:0.06951520666):0.08629203206):0.009999598291,G09_Mau3357:0.1658068374):0.007520571814):0.008609754352,A04_Mad502:0.1819371628):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-216.6083
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.6673786 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 1.5764627 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-150.1188
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.197684
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.6673786 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 1.5764627 1e-12 5.00000 1.108837e+00 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 2.949559e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.970504e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.980336e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.901681e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.901681e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.901681e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
Read 1 item
((((((((((((A01_Ald3:0.008717882127,((((A07_Ari1:0.001967840647,G02_Csne2:0.001967840644):0.0007102854424,H09_Csn1:0.002678126084):0.001005269798,C05_Dig2:0.00368339588):0.001731497168,B02_Sil4:0.005414893058):0.003302989095):0.001310740776,G01_Ald9:0.01002862289):0.001553450352,((C09_EUR51:0.005230355432,F06_JDN39:0.005230355434):0.004199077042,G10_GGL2:0.009429432451):0.002152640792):0.007511825678,H05_GRA5:0.01909389892):0.0009370332457,(E04_Mau270:0.009426887368,Reu_punc:0.009426887369):0.01060404485):0.004853792644,F01_Moh1:0.02488472487):0.06429646477,(((C04_Reu3837:0.02585956411,C06_Reu2000:0.02585956411):0.01143103954,D04_Reu4006:0.0372906036):0.02634495409,E09_Reu268:0.06363555774):0.02554563187):0.02222765827,(B03_Mau3189:0.06537895364,(((C02_Mau3202:0.02474193041,H08_Mau3333:0.02474193041):0.01225213479,H07_Mau3099:0.03699406524):0.01960645737,(D02_Mau3001:0.03222260289,Reu_mur:0.03222260289):0.02437791979):0.008778430989):0.04602989418):0.02576376517,(((A02_Reu3926:0.07951539052,G07_Mau3572:0.07951539052):0.02761589681,(F09_Mau3649:0.01632181546,H01_Reu3824:0.01632181546):0.09080947203):0.02033789842,((((A06_Mau3268:0.02928918021,B04_Reu3461:0.02928918021):0.0763282741,C08_Mau3856:0.1056174544):0.009054668489,(B08_Mau256:0.07827605256,((D09_Reu2041:0.009007268069,H06_Mau3721:0.009007268069):0.05394962501,(E06_Mau3680:0.04932084553,F07_Mau3606:0.0493208455):0.01363604756):0.01531915949):0.03639607015):0.002658692832,(B06_Mau3284:0.1032636596,(D07_Rod286:0.0448268835,F10_Rod164:0.0448268835):0.05843677586):0.01406715622):0.01013837033):0.009703427207):0.008810580396,(A09_Mau3863:0.1266576123,((C10_Reu2580:0.03868112934,G08_Mau3628:0.03868112934):0.07437013833,E10_Reu3072:0.1130512675):0.01360634464):0.01932558114):0.02687459721,((((((A11_Rod56:0.04414652617,H10_Rod124:0.04414652617):0.03781631422,H03_Rod257:0.08196284054):0.02756010973,((B05_Anj4:0.02210274478,G05_Anj3:0.02210274478):0.03730832551,G03_GRA10:0.05941107023):0.05011187994):0.02606948539,(((B01_Dig5:0.006239925371,D06_Pra6:0.006239925374):0.004327760166,B11_Mah1:0.01056768553):0.05630919387,F08_Dig6:0.0668768795):0.06871555614):0.0199619073,((B10_Reu2320:0.01339548159,E07_Mau3707:0.01339548159):0.05615628164,D01_Reu5116:0.0695517633):0.08600257971):0.009938965166,G09_Mau3357:0.1654933081):0.007364482456):0.008678669675,A04_Mad502:0.1815364605):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-216.7125
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.6880551 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 1.8612448 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-150.1863
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.269803
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.6880551 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 1.8612448 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 3.304789e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.966952e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.977968e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.889840e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.889840e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.889840e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
Read 1 item
((((((((((((A01_Ald3:0.008626403163,((((A07_Ari1:0.001920871955,G02_Csne2:0.001920871954):0.0007088571386,H09_Csn1:0.002629729099):0.001017724792,C05_Dig2:0.003647453896):0.001723568438,B02_Sil4:0.005371022342):0.003255380824):0.001265427596,G01_Ald9:0.009891830729):0.001510962279,((C09_EUR51:0.00517429705,F06_JDN39:0.005174297047):0.004193235237,G10_GGL2:0.009367532306):0.002035260733):0.007507880682,H05_GRA5:0.01891067368):0.0009448854115,(E04_Mau270:0.009407800249,Reu_punc:0.009407800247):0.01044775887):0.004877591953,F01_Moh1:0.02473315109):0.06457410407,(((C04_Reu3837:0.02574418104,C06_Reu2000:0.02574418104):0.01144594319,D04_Reu4006:0.03719012423):0.0262423082,E09_Reu268:0.0634324325):0.02587482265):0.02227985701,(B03_Mau3189:0.0655575847,(((C02_Mau3202:0.02471017522,H08_Mau3333:0.02471017523):0.01226722128,H07_Mau3099:0.03697739654):0.01984161508,(D02_Mau3001:0.03257009523,Reu_mur:0.03257009523):0.02424891636):0.008738573162):0.0460295274):0.02588496806,(((A02_Reu3926:0.07965646606,G07_Mau3572:0.07965646606):0.02768126626,(F09_Mau3649:0.01636286905,H01_Reu3824:0.01636286905):0.09097486359):0.02039611583,((((A06_Mau3268:0.02921295802,B04_Reu3461:0.02921295802):0.07673038357,C08_Mau3856:0.1059433416):0.008893797344,(B08_Mau256:0.07833486695,((D09_Reu2041:0.009001825293,H06_Mau3721:0.009001825293):0.05384605243,(E06_Mau3680:0.04937373372,F07_Mau3606:0.04937373369):0.01347414397):0.01548698928):0.03650227207):0.002735266818,(B06_Mau3284:0.1036998226,(D07_Rod286:0.04472380228,F10_Rod164:0.04472380228):0.05897602033):0.01387258323):0.01016144245):0.009738231925):0.008839281892,(A09_Mau3863:0.1264936217,((C10_Reu2580:0.03867047102,G08_Mau3628:0.03867047102):0.07415121525,E10_Reu3072:0.1128216864):0.01367193528):0.01981774055):0.02698862563,((((((A11_Rod56:0.04435941919,H10_Rod124:0.04435941919):0.03768876964,H03_Rod257:0.08204818875):0.02746721892,((B05_Anj4:0.02207843987,G05_Anj3:0.02207843987):0.0372361947,G03_GRA10:0.05931463458):0.05020077306):0.02620130033,(((B01_Dig5:0.006295912896,D06_Pra6:0.006295912882):0.00433655771,B11_Mah1:0.0106324706):0.0563058145,F08_Dig6:0.06693828515):0.06877842297):0.01988614626,((B10_Reu2320:0.01336939219,E07_Mau3707:0.01336939219):0.05644778122,D01_Reu5116:0.06981717343):0.08578568066):0.01027871914,G09_Mau3357:0.1658815733):0.007418414345):0.008663404657,A04_Mad502:0.1819633924):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-216.7628
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.2503015 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 4.8815300 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-161.2423
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.688556
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.2503015 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 4.8815300 1e-12 5.00000 1.667050e+00 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 3.568886e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.964311e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.976207e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.881037e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.881037e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.881037e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
Read 1 item
((((((((((((A01_Ald3:0.008733808737,((((A07_Ari1:0.001934656978,G02_Csne2:0.001934656984):0.0007321075601,H09_Csn1:0.002666764543):0.0009520110381,C05_Dig2:0.003618775576):0.001787447171,B02_Sil4:0.005406222741):0.003327585978):0.001347405575,G01_Ald9:0.01008121429):0.001535896294,((C09_EUR51:0.005214805829,F06_JDN39:0.005214805831):0.004276457031,G10_GGL2:0.009491262858):0.002125847745):0.007550721855,H05_GRA5:0.01916783245):0.0009406112558,(E04_Mau270:0.009499229848,Reu_punc:0.009499229848):0.01060921395):0.004829825259,F01_Moh1:0.024938269):0.06460165057,(((C04_Reu3837:0.02593461967,C06_Reu2000:0.02593461967):0.01156230339,D04_Reu4006:0.03749692304):0.02627834936,E09_Reu268:0.06377527242):0.02576464714):0.02221673071,(B03_Mau3189:0.06551929022,(((C02_Mau3202:0.02468939824,H08_Mau3333:0.02468939824):0.01205257352,H07_Mau3099:0.03674197169):0.01987288085,(D02_Mau3001:0.03228815105,Reu_mur:0.03228815105):0.02432670152):0.008904437685):0.04623735994):0.02570824632,(((A02_Reu3926:0.07960621754,G07_Mau3572:0.07960621754):0.02771591345,(F09_Mau3649:0.01637680829,H01_Reu3824:0.01637680829):0.09094532274):0.02044304848,((((A06_Mau3268:0.02925496779,B04_Reu3461:0.02925496779):0.0768628299,C08_Mau3856:0.1061177976):0.008994533992,(B08_Mau256:0.07848196296,((D09_Reu2041:0.009110329928,H06_Mau3721:0.009110329928):0.05380893341,(E06_Mau3680:0.04931864816,F07_Mau3606:0.04931864814):0.01360061507):0.01556269976):0.03663036868):0.002668750414,(B06_Mau3284:0.1037906999,(D07_Rod286:0.04492277285,F10_Rod164:0.04492277285):0.05886792707):0.01399038214):0.009984097576):0.009699716894):0.008774880183,(A09_Mau3863:0.1267768236,((C10_Reu2580:0.038703752,G08_Mau3628:0.038703752):0.07436366093,E10_Reu3072:0.113067413):0.01370941115):0.01946295253):0.0267627015,((((((A11_Rod56:0.0444634524,H10_Rod124:0.0444634524):0.03722249963,H03_Rod257:0.08168595206):0.02762476596,((B05_Anj4:0.02220438657,G05_Anj3:0.02220438657):0.03726567129,G03_GRA10:0.0594700579):0.04984066013):0.02635530018,(((B01_Dig5:0.006301715029,D06_Pra6:0.00630171503):0.004371745209,B11_Mah1:0.01067346022):0.05587618804,F08_Dig6:0.06654964825):0.06911636974):0.01982229955,((B10_Reu2320:0.01329910794,E07_Mau3707:0.01329910794):0.05634124847,D01_Reu5116:0.0696403564):0.08584796115):0.01017734694,G09_Mau3357:0.1656656647):0.007336813538):0.00862463752,A04_Mad502:0.1816271161):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-216.6566
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.7425363 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 4.2740616 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-152.4666
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.630841
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.7425363 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 4.2740616 1e-12 5.00000 3.765751e+00 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 3.331289e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.966687e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.977791e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.888957e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.888957e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.888957e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
Read 1 item
((((((((((((A01_Ald3:0.008730302645,((((A07_Ari1:0.001934655522,G02_Csne2:0.001934655524):0.0007529938972,H09_Csn1:0.00268764942):0.0009997016558,C05_Dig2:0.00368735108):0.001729705296,B02_Sil4:0.005417056383):0.003313246272):0.001274186637,G01_Ald9:0.01000448928):0.001536895575,((C09_EUR51:0.005151700974,F06_JDN39:0.005151700972):0.004287293592,G10_GGL2:0.009438994553):0.002102390275):0.007605183351,H05_GRA5:0.01914656822):0.0009219742814,(E04_Mau270:0.009508915029,Reu_punc:0.009508915031):0.01055962746):0.00484905962,F01_Moh1:0.02491760208):0.06479791331,(((C04_Reu3837:0.02591727396,C06_Reu2000:0.02591727396):0.01146607431,D04_Reu4006:0.0373833483):0.0266171658,E09_Reu268:0.06400051417):0.02571500135):0.02213898114,(B03_Mau3189:0.06581736148,(((C02_Mau3202:0.0249214979,H08_Mau3333:0.0249214979):0.01206049342,H07_Mau3099:0.03698199127):0.0197990637,(D02_Mau3001:0.03238366084,Reu_mur:0.03238366084):0.02439739414):0.009036306458):0.04603713511):0.0258281239,(((A02_Reu3926:0.07972483484,G07_Mau3572:0.07972483484):0.02784927681,(F09_Mau3649:0.01632992288,H01_Reu3824:0.01632992288):0.09124418895):0.0203551789,((((A06_Mau3268:0.02924140759,B04_Reu3461:0.02924140759):0.0771265004,C08_Mau3856:0.1063679075):0.008874280137,(B08_Mau256:0.07847827673,((D09_Reu2041:0.009075081093,H06_Mau3721:0.009075081093):0.05384845283,(E06_Mau3680:0.04911711465,F07_Mau3606:0.04911711462):0.01380641925):0.01555474285):0.03676391127):0.002666284405,(B06_Mau3284:0.1038579235,(D07_Rod286:0.04492124552,F10_Rod164:0.04492124552):0.05893667833):0.01405054857):0.01002081815):0.009753329976):0.00876618614,(A09_Mau3863:0.1270106968,((C10_Reu2580:0.0387611357,G08_Mau3628:0.0387611357):0.07485477442,E10_Reu3072:0.1136159099):0.01339478719):0.01943810935):0.02678474825,((((((A11_Rod56:0.04461000801,H10_Rod124:0.04461000801):0.03765188331,H03_Rod257:0.08226189139):0.02731104591,((B05_Anj4:0.02212183437,G05_Anj3:0.02212183437):0.0373897774,G03_GRA10:0.05951161178):0.05006132542):0.0263171016,(((B01_Dig5:0.006312767483,D06_Pra6:0.006312767477):0.004347727431,B11_Mah1:0.0106604949):0.05620718526,F08_Dig6:0.06686768022):0.06902235871):0.01977444716,((B10_Reu2320:0.01340943447,E07_Mau3707:0.01340943447):0.05628468698,D01_Reu5116:0.06969412142):0.08597036429):0.01026621113,G09_Mau3357:0.1659306975):0.007302857865):0.008753109838,A04_Mad502:0.1819866647):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-216.7069
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.6694012 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 1.6390200 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-150.1686
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.214584
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.6694012 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 1.6390200 1e-12 5.00000 5.122884e-01 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 3.363628e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.966364e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.977576e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.887879e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.887879e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.887879e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
Read 1 item
(((((((((((A01_Ald3:0.008660305845,((((A07_Ari1:0.001923031335,G02_Csne2:0.001923031334):0.000717616741,H09_Csn1:0.002640648081):0.001026884589,C05_Dig2:0.003667532669):0.001705493863,B02_Sil4:0.005373026524):0.003287279299):0.001339014376,G01_Ald9:0.009999320207):0.001553970588,((C09_EUR51:0.005206449676,F06_JDN39:0.005206449678):0.004272630188,G10_GGL2:0.009479079849):0.002074210931):0.008530782895,((E04_Mau270:0.009514214978,Reu_punc:0.009514214979):0.009415613045,H05_GRA5:0.018929828):0.001154245695):0.004894974116,F01_Moh1:0.02497904785):0.06409990904,(((C04_Reu3837:0.02574185219,C06_Reu2000:0.02574185219):0.01141239366,D04_Reu4006:0.03715424584):0.02625778962,E09_Reu268:0.06341203538):0.0256669214):0.02231483951,(B03_Mau3189:0.06530142763,(((C02_Mau3202:0.02468158978,H08_Mau3333:0.02468158978):0.01207854529,H07_Mau3099:0.0367601351):0.01965813248,(D02_Mau3001:0.03231456092,Reu_mur:0.03231456092):0.02410370655):0.008883160142):0.04609236877):0.02575251777,(((A02_Reu3926:0.07956051099,G07_Mau3572:0.07956051099):0.02770417418,(F09_Mau3649:0.01626697072,H01_Reu3824:0.01626697072):0.09099771445):0.02026867327,((((A06_Mau3268:0.02910430548,B04_Reu3461:0.02910430548):0.07679297439,C08_Mau3856:0.1058972804):0.008874216373,(B08_Mau256:0.07824799973,((D09_Reu2041:0.009163214822,H06_Mau3721:0.009163214822):0.05371081555,(E06_Mau3680:0.04928587932,F07_Mau3606:0.04928587926):0.01358815113):0.01537396933):0.0365234964):0.002703847518,(B06_Mau3284:0.1036429785,(D07_Rod286:0.0450019024,F10_Rod164:0.0450019024):0.05864107593):0.01383236546):0.01005801482):0.009612955606):0.008892411415,(A09_Mau3863:0.1267183108,((C10_Reu2580:0.03879039859,G08_Mau3628:0.03879039859):0.07425574403,E10_Reu3072:0.1130461428):0.01367216887):0.0193204141):0.02660717709,((((((A11_Rod56:0.04437170404,H10_Rod124:0.04437170404):0.03757333444,H03_Rod257:0.0819450385):0.02738511297,((B05_Anj4:0.02208894611,G05_Anj3:0.02208894611):0.03746727663,G03_GRA10:0.05955622267):0.04977392877):0.02619823223,(((B01_Dig5:0.006239848295,D06_Pra6:0.006239848284):0.004295120626,B11_Mah1:0.0105349689):0.05601031942,F08_Dig6:0.06654528832):0.06898309535):0.01961027453,((B10_Reu2320:0.01330276008,E07_Mau3707:0.01330276008):0.05626312436,D01_Reu5116:0.06956588442):0.08557277352):0.01011096366,G09_Mau3357:0.1652496222):0.007396280882):0.008722580581,A04_Mad502:0.1813684829):0;
Read 59 items
58 12 (A B C D E F G H I J K L)
A04_Mad502 100000000000
B04_Reu3461 010000000000
A01_Ald3 000000000010
G01_Ald9 000000000010
A07_Ari1 000000000001
G02_Csne2 000000000001
H09_Csn1 000000000001
G10_GGL2 000001000000
C09_EUR51 000010000000
F06_JDN39 000000000100
B02_Sil4 000000000001
C05_Dig2 000000000001
H05_GRA5 000000001000
E04_Mau270 001000000000
F01_Moh1 000000100000
A02_Reu3926 010000000000
G07_Mau3572 001000000000
H06_Mau3721 001000000000
D09_Reu2041 010000000000
B08_Mau256 001000000000
A09_Mau3863 001000000000
B03_Mau3189 001000000000
D02_Mau3001 001000000000
H08_Mau3333 001000000000
H07_Mau3099 001000000000
C02_Mau3202 001000000000
C08_Mau3856 001000000000
C10_Reu2580 010000000000
G08_Mau3628 001000000000
E10_Reu3072 010000000000
E06_Mau3680 001000000000
B06_Mau3284 001000000000
D04_Reu4006 010000000000
C04_Reu3837 010000000000
C06_Reu2000 010000000000
E09_Reu268 010000000000
A11_Rod56 000100000000
H10_Rod124 000100000000
D01_Reu5116 010000000000
B10_Reu2320 010000000000
E07_Mau3707 001000000000
H01_Reu3824 010000000000
F09_Mau3649 001000000000
F10_Rod164 000100000000
D07_Rod286 000100000000
B01_Dig5 000000000001
D06_Pra6 000000000001
B11_Mah1 000000000001
F08_Dig6 000000000001
B05_Anj4 000000010000
G05_Anj3 000000010000
G03_GRA10 000000001000
G09_Mau3357 001000000000
F07_Mau3606 001000000000
H03_Rod257 000100000000
Reu_mur 010000000000
Reu_punc 010000000000
A06_Mau3268 001000000000
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1 1
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-217.2013
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 0
$lbratio
[1] 0
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.01000 1e-12 5.00000 0.8202114 works anagenesis: rate of 'dispersal' (range expansion)
e free 0.01000 1e-12 5.00000 5.0000000 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.00000 1e-12 5.00000 0.0000000 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.00000 1e-12 1.00000 1.0000000 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on distance (modifies d, j, a)
n fixed 0.00000 -1e+01 10.00000 0.0000000 works exponent on environmental distance (modifies d, j, a)
w fixed 1.00000 -1e+01 10.00000 1.0000000 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.00000 -1e+01 10.00000 0.0000000 works anagenesis: exponent on extinction risk with area (modifies e)
j fixed 0.00000 1e-05 2.99999 0.0000000 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.99999 1e-05 3.00000 3.0000000 works cladogenesis: y+s+v
ys ysv*2/3 1.99999 1e-05 2.00000 2.0000000 works cladogenesis: y+s
y ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.00000 1e-05 1.00000 1.0000000 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.00010 1e-04 0.99990 0.0001000 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.50000 1e-04 0.99990 0.5000000 no root: controls range size probabilities of root
mf fixed 0.10000 5e-03 0.99500 0.1000000 yes mean frequency of truly sampling OTU of interest
dp fixed 1.00000 5e-03 0.99500 1.0000000 yes detection probability per true sample of OTU of interest
fdp fixed 0.00000 5e-03 0.99500 0.0000000 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Read 14 items
Read 116 items
Your computer has 8 cores.
[1] "parscale:"
[1] 1.666678 1.666678 1.000000
NOTE: Before running optimx(), here is a test calculation of the data likelihood
using calc_loglike_for_optim() on initial parameter values...
if this crashes, the error messages are more helpful
than those from inside optimx().
calc_loglike_for_optim() on initial parameters loglike=-153.9184
Calculation of likelihood on initial parameters: successful.
Now starting Maximum Likelihood (ML) parameter optimization with optimx()...
Printing any warnings() that occurred during calc_loglike_for_optim():
NULL
Results of optimx:::scalecheck() below. Note: sometimes rescaling parameters may be helpful for ML searches, when the parameters have much different absolute sizes. This can be attempted by setting BioGeoBEARS_run_object$rescale_params = TRUE.
$lpratio
[1] 4.69897
$lbratio
[1] 0.2218516
Maximizing -- use negfn and neggr
This is the output from optim, optimx, or GenSA. Check the help on those functions to
interpret this output and check for convergence issues:
Reading the optim/optimx/GenSA output into the BioGeoBEARS_model object:
BioGeoBEARS_model_object =
An object of class "BioGeoBEARS_model"
Slot "params_table":
type init min max est note desc
d free 0.8202114 1e-12 5.00000 1.000000e-12 works anagenesis: rate of 'dispersal' (range expansion)
e free 5.0000000 1e-12 5.00000 6.826476e-01 works anagenesis: rate of 'extinction' (range contraction)
a fixed 0.0000000 1e-12 5.00000 0.000000e+00 works anagenesis: rate of range-switching (i.e. for a standard char.)
b fixed 1.0000000 1e-12 1.00000 1.000000e+00 non-stratified only anagenesis: exponent on branch lengths
x fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on distance (modifies d, j, a)
n fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works exponent on environmental distance (modifies d, j, a)
w fixed 1.0000000 -1e+01 10.00000 1.000000e+00 works exponent on manual dispersal multipliers (modifies d, j, a)
u fixed 0.0000000 -1e+01 10.00000 0.000000e+00 works anagenesis: exponent on extinction risk with area (modifies e)
j free 0.0001000 1e-05 2.99999 3.299617e-02 works cladogenesis: relative per-event weight of jump dispersal
ysv 3-j 2.9999900 1e-05 3.00000 2.967004e+00 works cladogenesis: y+s+v
ys ysv*2/3 1.9999900 1e-05 2.00000 1.978003e+00 works cladogenesis: y+s
y ysv*1/3 1.0000000 1e-05 1.00000 9.890013e-01 works cladogenesis: relative per-event weight of sympatry (range-copying)
s ysv*1/3 1.0000000 1e-05 1.00000 9.890013e-01 works cladogenesis: relative per-event weight of subset speciation
v ysv*1/3 1.0000000 1e-05 1.00000 9.890013e-01 works cladogenesis: relative per-event weight of vicariant speciation
mx01 fixed 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01j mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01y mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01s mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01v mx01 0.0001000 1e-04 0.99990 1.000000e-04 works cladogenesis: controls range size of smaller daughter
mx01r fixed 0.5000000 1e-04 0.99990 5.000000e-01 no root: controls range size probabilities of root
mf fixed 0.1000000 5e-03 0.99500 1.000000e-01 yes mean frequency of truly sampling OTU of interest
dp fixed 1.0000000 5e-03 0.99500 1.000000e+00 yes detection probability per true sample of OTU of interest
fdp fixed 0.0000000 5e-03 0.99500 0.000000e+00 yes false detection of OTU probability per true taphonomic control sample
...successful.
Uppass starting for marginal ancestral states estimation!
Uppass completed for marginal ancestral states estimation!
NOTE: multiple states tied
Note: in get_ML_probs(), picking the first state in the tie; use unlist_TF=FALSE to see all states.
Couldn't get a file descriptor referring to the console
write.table(unlist_df(teststable), file="Metadata/BioGeoBEARS_save_files/Teststable.txt", quote=FALSE, sep="\t")
Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, :
arguments imply differing number of rows: 0, 10